Gemma 2 27B IT GGUF — Hardware Requirements & GPU Compatibility
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7.4K downloads
174 likesSpecifications
- Publisher
- Bartowski
- Family
- Gemma 2
- Parameters
- 27B
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Gemma 2 27B IT GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 12.6 GB | — | 11.47 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 13.0 GB | — | 11.81 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 14.5 GB | — | 13.16 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 17.8 GB | — | 16.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 21.2 GB | — | 19.24 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 24.5 GB | — | 22.27 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 29.7 GB | — | 27.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 2 27B IT GGUF?
Q4_K_M · 17.8 GBGemma 2 27B IT GGUF (Q4_K_M) requires 17.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 24+ GB is recommended. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Gemma 2 27B IT GGUF?
Q4_K_M · 17.8 GB21 devices with unified memory can run Gemma 2 27B IT GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 2 27B IT GGUF need?
Gemma 2 27B IT GGUF requires 17.8 GB of VRAM at Q4_K_M, or 29.7 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 27B × 4.8 bits ÷ 8 = 16.2 GB
KV Cache + Overhead ≈ 1.6 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M17.8 GB- Can NVIDIA GeForce RTX 4090 run Gemma 2 27B IT GGUF?
Yes, at Q5_K_L (21.5 GB) or lower. Higher quantizations like Q6_K (24.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Gemma 2 27B IT GGUF?
For Gemma 2 27B IT GGUF, Q4_K_M (17.8 GB) offers the best balance of quality and VRAM usage. Q4_K_L (18.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 8.9 GB.
VRAM requirement by quantization
IQ2_XS8.9 GB~57%Q2_K12.6 GB~75%Q3_K_L15.2 GB~86%Q4_K_M ★17.8 GB~89%Q5_K_S20.4 GB~92%Q8_029.7 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 2 27B IT GGUF on a Mac?
Gemma 2 27B IT GGUF requires at least 8.9 GB at IQ2_XS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Gemma 2 27B IT GGUF locally?
Yes — Gemma 2 27B IT GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 17.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 2 27B IT GGUF?
At Q4_K_M, Gemma 2 27B IT GGUF can reach ~164 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~37 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 17.8 × 0.55 = ~164 tok/s
Estimated speed at Q4_K_M (17.8 GB)
AMD Instinct MI300X~164 tok/sNVIDIA GeForce RTX 4090~37 tok/sNVIDIA H100 SXM~122 tok/sAMD Instinct MI250X~101 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gemma 2 27B IT GGUF?
At Q4_K_M, the download is about 16.20 GB. The full-precision Q8_0 version is 27.00 GB. The smallest option (IQ2_XS) is 8.10 GB.
- Which GPUs can run Gemma 2 27B IT GGUF?
6 consumer GPUs can run Gemma 2 27B IT GGUF at Q4_K_M (17.8 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Gemma 2 27B IT GGUF?
21 devices with unified memory can run Gemma 2 27B IT GGUF at Q4_K_M (17.8 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.